Fast simulation-driven antenna design using response-feature surrogates

• Published in: International journal of RF and microwave computer-aided engineering Vol. 25; no. 5; pp. 394 - 402
• Main Author: Koziel, Slawomir
• Format: Journal Article
• Language: English
• Published: Hoboken Blackwell Publishing Ltd 01.06.2015; John Wiley & Sons, Inc
• Subjects: antenna optimization; Antennas; Approximation; Computational efficiency; Computer simulation; computer-aided design; design automation; electromagnetic-driven design; Mathematical analysis; Mathematical models; Microwaves; Optimization; response features; surrogate modeling
• ISSN: 1096-4290; 1099-047X
• DOI: 10.1002/mmce.20873

ABSTRACT In this article, a computationally efficient procedure for electromagnetic (EM)‐simulation‐driven design of antennas is presented. Our methodology is based on local approximation models of the antenna response, established using a set of suitably selected characteristic features rather than the entire response (such as reflection versus frequency). The approximation model is utilized to verify the level of satisfying/violating given performance requirements, and to guide the optimization process towards a better design. By exploiting the fact that the dependence of the response features on the designable parameters of the antenna of interest is simple (close to linear or quadratic), the feature‐based optimization converges faster than conventional optimization of frequency‐based EM‐simulated responses. In order to further speed up the design, coarse‐discretization simulations are utilized to estimate the feature gradients with respect to adjustable parameters of the problem at hand. The optimization algorithm is embedded in the trust‐region framework for safeguarding convergence. The proposed technique is demonstrated using two antenna examples. In both the cases, the optimum design is obtained at the computational cost corresponding to a few high‐fidelity EM antenna simulations. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:394–402, 2015.